Isochronen in Dresden

Für die Erklärung bitte hier gucken:

Scenario Beschreibungen:

• Für eine Detailierte Beschreibung bitte in beschreibung-szenarien gucken.

• tl;dr

  • Small: Nur Sidewalks

  • Medium: Alles außer Große Straßen

  • Medium-restricted: Sidewalks und Radwege

  • Large: Nur Straßen keine Fuß und Radwege

  • Optimal: Alles

cfg = ConfigObj("notebook.ini")
place = "Dresden, Sachsen"
trip_times = [5, 10, 15, 20, 25, 30]  # in minutes
travel_speed = 3.6  # in km/h

ox.settings.useful_tags_way += cfr.get_relevant_tags(cfg)
# ox.config(log_console=True, use_cache=True)

Alle Straßen

Download and prep the street network

# download the street network
# G_84 = r4r.get_graph(place)

latitude = 51.047993772661066 
longitude = 13.74043214154367
G_84 = ox.graph_from_point((latitude, longitude),
                           dist=1500, network_type='all')

# find the centermost node and then project the graph to UTM
# gdf_nodes = ox.graph_to_gdfs(G_84, edges=False)
# x, y = gdf_nodes['geometry'].unary_union.centroid.xy
# center_node = ox.nearest_nodes(G_84, x[0], y[0])
# center_node = ox.nearest_nodes(G_84, 13.3447448, 50.9162500)
center_node = ox.nearest_nodes(
    G_84, longitude, latitude)  # Dresden, Rathaus
# center_node = 3930896811
G = ox.project_graph(G_84)

center_node

403203636

# add an edge attribute for time in minutes required to traverse each edge
meters_per_minute = travel_speed * 1000 / 60  # km per hour to m per minute
for u, v, k, data in G.edges(data=True, keys=True):
    data['time'] = data['length'] / meters_per_minute

# get one color for each isochrone
iso_colors = ox.plot.get_colors(
    n=len(trip_times), cmap='plasma', start=0)

# color the nodes according to isochrone then plot the street network
node_colors = {}
for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(G, center_node, radius=trip_time, distance='time')
    for node in subgraph.nodes():
        node_colors[node] = color
nc = [node_colors[node] if node in node_colors else 'none' for node in G.nodes()]
ns = [15 if node in node_colors else 0 for node in G.nodes()]
fig, ax = ox.plot_graph(G, node_color=nc, node_size=ns, node_alpha=0.8, node_zorder=2,
                        bgcolor='k', edge_linewidth=0.2, edge_color='#999999', figsize=(16, 16))

Plot the time-distances as isochrones

How far can you walk in 5, 10, 15, 20, and 25 minutes from the origin node? We’ll use a convex hull, which isn’t perfectly accurate. A concave hull would be better, but shapely doesn’t offer that.

# make the isochrone polygons
isochrone_polys = []
for trip_time in sorted(trip_times, reverse=True):
    subgraph = nx.ego_graph(G, center_node, radius=trip_time, distance='time')
    node_points = [Point((data['x'], data['y']))
                   for node, data in subgraph.nodes(data=True)]
    bounding_poly = gpd.GeoSeries(node_points).unary_union.convex_hull
    isochrone_polys.append(bounding_poly)

# First, plot the network
fig, ax = ox.plot_graph(G, show=False, close=False,
                        edge_color='#999999', edge_alpha=0.2, node_size=0, bgcolor='k')

# Now, plot each isochrone polygon
for polygon, color in zip(isochrone_polys, iso_colors):
    # Extract the exterior coordinates of the polygon
    x, y = polygon.exterior.xy
    ax.fill(x, y, fc=color, ec='none', alpha=0.6, zorder=-1)

    # If there are interiors (holes) in the polygon, plot them too
    for interior in polygon.interiors:
        x, y = interior.xy
        # Fill the hole with the background color (in this case, black)
        ax.fill(x, y, fc='k', ec='none', alpha=0.6)

plt.show()

Or, plot isochrones as buffers to get more faithful isochrones than convex hulls can offer

def make_iso_polys(G, edge_buff=25, node_buff=50, infill=False):
    isochrone_polys = []
    for trip_time in sorted(trip_times, reverse=True):
        subgraph = nx.ego_graph(
            G, center_node, radius=trip_time, distance='time')

        node_points = [Point((data['x'], data['y']))
                       for node, data in subgraph.nodes(data=True)]
        nodes_gdf = gpd.GeoDataFrame(
            {'id': subgraph.nodes()}, geometry=node_points)
        nodes_gdf = nodes_gdf.set_index('id')

        edge_lines = []
        for n_fr, n_to in subgraph.edges():
            f = nodes_gdf.loc[n_fr].geometry
            t = nodes_gdf.loc[n_to].geometry
            edge_data = G.get_edge_data(n_fr, n_to).get(0, None)
            if edge_data is None:
                continue
            edge_lookup = edge_data.get(
                'geometry',  LineString([f, t]))
            edge_lines.append(edge_lookup)

        n = nodes_gdf.buffer(node_buff).geometry
        e = gpd.GeoSeries(edge_lines).buffer(edge_buff).geometry
        all_gs = list(n) + list(e)
        new_iso = gpd.GeoSeries(all_gs).unary_union

        # try to fill in surrounded areas so shapes will appear solid and blocks without white space inside them
        if infill:
            new_iso = Polygon(new_iso.exterior)
        isochrone_polys.append(new_iso)
    return isochrone_polys


# Plotting section
isochrone_polys = make_iso_polys(G, edge_buff=25, node_buff=0, infill=True)
fig, ax = ox.plot_graph(G, show=False, close=False,
                        edge_color='#999999', edge_alpha=0.2, node_size=0, bgcolor='k')

# Plot each isochrone polygon
for polygon, color in zip(isochrone_polys, iso_colors):
    # Extract the exterior coordinates of the polygon
    x, y = polygon.exterior.xy
    ax.fill(x, y, fc=color, ec='none', alpha=0.6, zorder=-1)

    # If there are interiors (holes) in the polygon, plot them too
    for interior in polygon.interiors:
        x, y = interior.xy
        # Fill the hole with the background color (in this case, black)
        ax.fill(x, y, fc='k', ec='none', alpha=0.6)

plt.show()

def make_iso_polys_time(G, edge_buff=25, node_buff=50, infill=False, time_window=15):
    # Assume trip_times is a list of time values (in minutes) you're interested in
    trip_times = [time_window]

    isochrone_polys = []

    for trip_time in trip_times:
        subgraph = nx.ego_graph(
            G, center_node, radius=trip_time, distance='time')

        node_points = [Point((data['x'], data['y']))
                       for node, data in subgraph.nodes(data=True)]
        nodes_gdf = gpd.GeoDataFrame(
            {'id': subgraph.nodes()}, geometry=node_points)
        nodes_gdf = nodes_gdf.set_index('id')

        edge_lines = []
        for n_fr, n_to in subgraph.edges():
            f = nodes_gdf.loc[n_fr].geometry
            t = nodes_gdf.loc[n_to].geometry
            edge_lookup = G.get_edge_data(n_fr, n_to).get(0,  None)
            if edge_lookup is None:
                continue
            edge_lookup = edge_lookup.get(
                'geometry',  LineString([f, t]))
            edge_lines.append(edge_lookup)

        n = nodes_gdf.buffer(node_buff).geometry
        e = gpd.GeoSeries(edge_lines).buffer(edge_buff).geometry
        all_gs = list(n) + list(e)
        new_iso = gpd.GeoSeries(all_gs).unary_union

        if infill:
            new_iso = Polygon(new_iso.exterior)

        isochrone_polys.append(new_iso)

    return isochrone_polys

def convert_coordinates(geom):
    """Convert UTM Polygon/MultiPolygon to Lat/Lon Polygon/MultiPolygon."""

    # Define the UTM zone projection and the lat/long projection
    utm_zone = "EPSG:32633"  # Example for UTM zone 33N, change as per your UTM zone
    latlong = "EPSG:4326"
    # Change zone accordingly
    in_proj = Proj(proj='utm', zone=33, datum='WGS84')
    out_proj = Proj(proj='latlong', datum='WGS84')

    if geom.geom_type == 'Polygon':
        exterior_coords = [transform(in_proj, out_proj, x, y)
                           for x, y in geom.exterior.coords]
        interior_coords = [[transform(in_proj, out_proj, x, y)
                            for x, y in interior.coords] for interior in geom.interiors]
        return Polygon(exterior_coords, holes=interior_coords)
    elif geom.geom_type == 'MultiPolygon':
        polygons = []
        for polygon in geom:
            polygons.append(convert_coordinates(polygon))
        return MultiPolygon(polygons)
    else:
        raise ValueError(f"Unsupported geometry type: {geom.geom_type}")


converted_isochrone_polys = [convert_coordinates(
    polygon) for polygon in isochrone_polys]

# m = ox.graph_to_gdfs(G_84, nodes=False).explore()
# m

# m = folium.Map(location=[converted_isochrone_polys[0].centroid.y,
#                converted_isochrone_polys[0].centroid.x], zoom_start=14)
m = ox.graph_to_gdfs(G_84, nodes=False).explore()

center_point = [latitude, longitude]

# Add each isochrone polygon to the map
for polygon, color in zip(converted_isochrone_polys, iso_colors):
    folium.Polygon(
        locations=[(lat, lon) for lon, lat in polygon.exterior.coords],
        fill=False,
        # fill_opacity=0.05,
        # fill_color=color,
        color=color,
        weight=5
    ).add_to(m)

folium.Circle(
    location=center_point,
    radius=900,  # radius in meters
    color='green',
    fill=False,
    # fill_color='blue',
    # fill_opacity=0.05
).add_to(m)

# Add a circle with 2km radius
folium.Circle(
    location=center_point,
    radius=2000,  # radius in meters
    color='blue',
    fill=False,
    # fill_color='blue',
    # fill_opacity=0.05
).add_to(m)

# Add a circle with 2km radius
folium.Circle(
    location=center_point,
    radius=2600,  # radius in meters
    color='red',
    fill=False,
    # fill_color='blue',
    # fill_opacity=0.05
).add_to(m)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

def make_folium(G_84, converted_isochrone_polys, iso_colors, center_point = [0,0]):
    # Add each isochrone polygon to the map
    m = ox.graph_to_gdfs(G_84, nodes=False).explore()

    for polygon, color in zip(converted_isochrone_polys, iso_colors):
        folium.Polygon(
            locations=[(lat, lon) for lon, lat in polygon.exterior.coords],
            fill=False,
            # fill_opacity=0.1,
            # fill_color=color,
            color=color,
            weight=5
        ).add_to(m)

    # Add a  circle with 2km radius
    folium.Circle(
        location=center_point,
        radius=900,  # radius in meters
        color='green',
        fill=False,
        # fill_color='blue',
        # fill_opacity=0.05
    ).add_to(m)

    # Add a circle with 2km radius
    folium.Circle(
        location=center_point,
        radius=2000,  # radius in meters
        color='blue',
        fill=False,
        # fill_color='blue',
        # fill_opacity=0.05
    ).add_to(m)

    # Add a circle with 2km radius
    folium.Circle(
        location=center_point,
        radius=2600,  # radius in meters
        color='red',
        fill=False,
        # fill_color='blue',
        # fill_opacity=0.05
    ).add_to(m)

    return m

# Get the Graph
def prep_graph(place, szenario, travel_speed=3.6, center_point=[13.3447448, 50.9162500]):

    G_84 = r4r.get_graph(place)

    scenario_filter = r4r.get_scenario_filter(cfg, szenario["name"])
    H_84 = oh.filter_graph_by_dict(
        G_84.copy(), scenario_filter, drop_nodes=False)

    # center_node = ox.nearest_nodes(H_84, center_point[0], center_point[1])
    H = ox.project_graph(H_84)

    # add an edge attribute for time in minutes required to traverse each edge
    meters_per_minute = travel_speed * 1000 / 60  # km per hour to m per minute
    for u, v, k, data in H.edges(data=True, keys=True):
        data['time'] = data['length'] / meters_per_minute

    return H, H_84

szenarios = list(cfr.yield_scenarios(cfg))

Small

• Small: Nur Sidewalks

# Filter Graph as per Szenario
# Plot Isochrones
H1, H1_84 = prep_graph(place, szenarios[0], travel_speed)
# This is to big for Jupyter-book so please try it locally!
# isochrone_polys = make_iso_polys(H1, edge_buff=25, node_buff=0, infill=True)
# converted_isochrone_polys = [convert_coordinates(
#     polygon) for polygon in isochrone_polys]
# m = make_folium(H1_84, converted_isochrone_polys, iso_colors, center_point)
# m

Done Loading Graph...

# color the nodes according to isochrone then plot the street network
node_colors = {}
for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(H1, center_node, radius=trip_time, distance='time')
    for node in subgraph.nodes():
        node_colors[node] = color
nc = [node_colors[node] if node in node_colors else 'none' for node in H1.nodes()]
ns = [15 if node in node_colors else 0 for node in H1.nodes()]
fig
fig, ax = ox.plot_graph(H1, node_color=nc, node_size=ns, node_alpha=0.8, node_zorder=2,
                        bgcolor='k', edge_linewidth=0.2, edge_color='#999999', figsize=(16, 16))

Medium

• Medium: Alles außer große Straßen

H1, H1_84 = prep_graph(place, szenarios[1], travel_speed)
# This is to big for Jupyter-book so please try it locally!
# isochrone_polys = make_iso_polys(H1, edge_buff=25, node_buff=0, infill=True)
# converted_isochrone_polys = [convert_coordinates(
#     polygon) for polygon in isochrone_polys]
# m = make_folium(H1_84, converted_isochrone_polys, iso_colors, center_point)
# m

Done Loading Graph...

# color the nodes according to isochrone then plot the street network
node_colors = {}
for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(H1, center_node, radius=trip_time, distance='time')
    for node in subgraph.nodes():
        node_colors[node] = color
nc = [node_colors[node] if node in node_colors else 'none' for node in H1.nodes()]
ns = [15 if node in node_colors else 0 for node in H1.nodes()]
fig
fig, ax = ox.plot_graph(H1, node_color=nc, node_size=ns, node_alpha=0.8, node_zorder=2,
                        bgcolor='k', edge_linewidth=0.2, edge_color='#999999', figsize=(16, 16))

Medium-restricted

• Medium-restricted: Sidewalks und Radwege

H1, H1_84 = prep_graph(place, szenarios[2], travel_speed)
# This is to big for Jupyter-book so please try it locally!
# isochrone_polys = make_iso_polys(H1, edge_buff=25, node_buff=0, infill=True)
# converted_isochrone_polys = [convert_coordinates(
#     polygon) for polygon in isochrone_polys]
# m = make_folium(H1_84, converted_isochrone_polys, iso_colors, center_point)
# m

Done Loading Graph...

# color the nodes according to isochrone then plot the street network
node_colors = {}
for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(H1, center_node, radius=trip_time, distance='time')
    for node in subgraph.nodes():
        node_colors[node] = color
nc = [node_colors[node] if node in node_colors else 'none' for node in H1.nodes()]
ns = [15 if node in node_colors else 0 for node in H1.nodes()]
fig
fig, ax = ox.plot_graph(H1, node_color=nc, node_size=ns, node_alpha=0.8, node_zorder=2,
                        bgcolor='k', edge_linewidth=0.2, edge_color='#999999', figsize=(16, 16))

Large

• Large: Nur Straßen keine Fuß und Radwege

H1, H1_84 = prep_graph(place, szenarios[3], travel_speed)
# This is to big for Jupyter-book so please try it locally!
# isochrone_polys = make_iso_polys(H1, edge_buff=25, node_buff=0, infill=True)
# converted_isochrone_polys = [convert_coordinates(
#     polygon) for polygon in isochrone_polys]
# m = make_folium(H1_84, converted_isochrone_polys, iso_colors, center_point)
# m

Done Loading Graph...

# color the nodes according to isochrone then plot the street network
node_colors = {}
for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):
    subgraph = nx.ego_graph(H1, center_node, radius=trip_time, distance='time')
    for node in subgraph.nodes():
        node_colors[node] = color
nc = [node_colors[node] if node in node_colors else 'none' for node in H1.nodes()]
ns = [15 if node in node_colors else 0 for node in H1.nodes()]
fig
fig, ax = ox.plot_graph(H1, node_color=nc, node_size=ns, node_alpha=0.8, node_zorder=2,
                        bgcolor='k', edge_linewidth=0.2, edge_color='#999999', figsize=(16, 16))

Optimal

• Optimal: Alles

Selbe wie hier oben

# H5, H5_84 = prep_graph(place, szenarios[4], travel_speed)
# isochrone_polys = make_iso_polys(H5, edge_buff=25, node_buff=0, infill=True)
# converted_isochrone_polys = [convert_coordinates(
#     polygon) for polygon in isochrone_polys]
# m = make_folium(H_84, converted_isochrone_polys, iso_colors, center_point)
# m